Swiggy Achieves 43% Cost Savings with AWS DMS and Amazon RDS

Overview

Swiggy's 180+ RDS servers hit performance bottlenecks from 150GB+ tables blocking rapid expansion to 500+ cities. Mixed replication + inconsistent backups created operational chaos during festival peaks. Slow queries and fragmentation caused order delays hurting customer satisfaction. Legacy instances drove massive cost overruns during growth phase. Mydbops DMS migration standardized infrastructure seamlessly. Peak rush scalability restored instantly.

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$54

ARR Savings

Through instance rightsizing and archival strategies.

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<0.5s

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Query Performance

Reduced search response time.

<10 Mins

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Minimal Downtime

Ensured uninterrupted order processing.

800

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Storage Reclaimed

Freed per server by defragmentation

RDS MySQL

Consulting Services

About

Swiggy runs India's top food delivery service, connecting 120,000+ restaurants to millions of orders every month nationwide. They handle massive daily volumes to get food to doorsteps fast in every city. Trusted by 50M+ active users for quick, reliable delivery every single day. Their platform powers nonstop growth across urban and tier-2 markets alike.

★★★★★

Deployment Type

Database Stack

Outcome

Cloud-Based Deployment

RDS MySQL using AWS DMS

DB costs reduced by 34–43%

Deployment Type

Cloud-Based Deployment

Database Stack

RDS MySQL using AWS DMS

Outcome

DB costs reduced by 34–43%

Business Challenges

Overview

Swiggy’s database environment included 180+ MySQL RDS servers deployed in multi-AZ, single-AZ replicas, and standalone nodes. This setup created several challenges:

Large Data Volumes: Tables exceeding 150 GB slowed schema changes and impacted agility.

Operational Complexity: Mixed replication setups and inconsistent backup policies increased management overhead.

Performance Bottlenecks: Slow queries, replication lag, and table fragmentation affected user experience.

Cost Overruns: Legacy instances and inefficient storage usage inflated infrastructure costs.

Limited Scalability: The architecture could not reliably handle concurrency spikes during promotions or festive seasons.

Goals

The key objectives the client was aiming to achieve:

Risks if Not Addressed

If left unresolved, these challenges posed serious risks

Risks & Impact if Not Addressed

Performance Issues

Without resolving replication lag and fragmented tables, query performance would continue to degrade, leading to a frustrating customer experience during peak hours.

Business Continuity Risks

Non-standardized backup policies increased the risk of data loss and prolonged outages, potentially disrupting thousands of orders in real-time.

Revenue Loss

Poor performance and downtime during peak times directly impacted Swiggy’s ability to fulfill customer demand, resulting in lost revenue and dissatisfied users.

Escalating Costs

Continued reliance on oversized, under-optimized infrastructure would lead to unnecessary monthly spend, straining the company’s profitability.

Developer Inefficiency

Lack of a stable and scalable database foundation meant developers spent significant time firefighting performance issues instead of innovating on features.

Performance Issues: Replication lag and fragmentation slow order searches and transactions.
Business Continuity Risks: Non-standardized backups mean longer recovery times and higher data-loss risk.
Revenue Loss: Slow page loads or timeouts during peak hours lead to failed checkouts.
Escalating Costs: Over provisioned, under-optimized servers strain profitability
Developer Inefficiency: Engineers spend more time firefighting than building new features

Goals

The key objectives the client was aiming to achieve:

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[Goal 1]

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[Goal 1]

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[Goal 1]

Solution Provided by Mydbops

Swiggy partnered with Mydbops to modernize the RDS environment while minimizing downtime and business impact. The strategy leveraged AWS DMS (Full Load + CDC) to migrate and optimized databases performance. The approach allowed Swiggy to keep MySQL RDS as the engine but transform the schema, optimize performance, and reduce storage footprint while the old environment remained live. By the time of cutover, traffic shifted seamlessly to the new, leaner, and more efficient database infrastructure.

Before Migration‍:

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Conducted environment assessment and schema validation to identify bottlenecks and fragmentation.

Provisioned a permanent Bastion server for controlled DB access.
Conducted environment assessment and schema validation to identify bottlenecks and fragmentation.
Configured limited-privilege database users manually for security.
Stored pre-assessment reports in Amazon S3 for pre-validation.
Built migration playbooks and rollback plans to ensure repeatability.

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During Migration:

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Configured AWS DMS tasks (Full Load + CDC) for continuous replication.
Applied data transformation and archival strategies to reduce live data footprint.
Performed manual row-level and schema validation; validation scripts were not stored in S3.
Executed cutover during non-peak hours with <10 minutes downtime.
Continuously monitored via Amazon CloudWatch; Nagios was used for external validation alerts.

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After Migration:

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Optimized queries, schema changes on bigger tables and defragmentation, reclaiming ~800 GB.
Rightsized RDS instances for a balance of performance and cost, achieving 34–40% savings.
Standardized backup and recovery policies for operational consistency.
Delivered knowledge transfer sessions to customer teams.
Transitioned to 24×7 Managed Services for ongoing monitoring, alerting, and support.

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‍AWS Services Used:

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Amazon RDS for MySQL – Fully managed relational database with Multi-AZ deployment.
AWS DMS – For seamless migration using Full Load + CDC.
‍Amazon S3 – Storage for pre-check validation reports, test outputs, and free check validation.

Amazon CloudWatch – Centralized monitoring and alerting.

Alignment with AWS Well-Architected Framework:

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Operational Excellence: Documented playbooks, pre-migration reports in S3, and proactive monitoring ensured repeatable, structured operations.
Security: Permanent Bastion access and manually configured least-privilege DB users safeguarded data throughout.
Reliability: DMS minimized downtime and ensured consistency; Multi-AZ RDS enabled automatic failover.
Performance Efficiency: Defragmentation and index tuning reduced row scans and improved query response times.
Cost Optimization: Rightsizing and archival strategies reduced annual costs while maintaining performance.
Sustainability: Optimized resource usage lowered infrastructure footprint and energy consumption.

Results and Impact

Key Outcomes

✅ Minimal Downtime

Migration cutover achieved with <10 minutes downtime, ensuring uninterrupted order processing.

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✅ Performance Gains

Queries that previously took 1-2 seconds now respond in under 0.5 seconds.

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✅ Storage Efficiency

DB defragmentation reclaimed ~800 GB per server.

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✅ ‍High Availability

Multi-AZ deployment ensured resilience and uptime.

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✅ ‍ARR Savings

Rightsizing and archival led to $54K ARR savings (42.5%), reflecting long-term cost efficiency.

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✅ ‍Operational Efficiency

Standardized backups, monitoring, and runbooks freed developers to focus on new features.

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✅ ‍Future-Readiness

The new architecture can handle traffic spikes during festivals and promotions without impact.

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Why Swiggy Chose Mydbops

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Deep Expertise in Large-Scale MySQL Environments – Proven success in handling 100+ node clusters and petabyte-scale workloads.

‍Minimal Downtime Promise – Strong experience with AWS DMS migrations ensuring cutovers with negligible business impact.‍‍

‍End-to-End Ownership – From pre-assessment to post-migration managed services, providing a one-stop solution.

‍‍Outcome-Focused Approach – Clear alignment with business goals of performance, cost savings, and scalability.

Trusted AWS Partner – Demonstrated capabilities mapped to the AWS Well-Architected Framework.

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Swiggy’s transformation wasn’t just about optimizing databases — it was about restoring confidence, speed, and reliability to millions of food deliveries happening every day. Behind every successful order is a seamless data flow — a story of precision engineering ensuring that hungry customers get what they love, on time.

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At Mydbops, we don’t just tune queries or manage clusters — we craft resilient data experiences that let businesses like Swiggy scale without compromise. Because when technology runs effortlessly, customers feel it — in every tap, every order, every moment of satisfaction.

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Ready to deliver seamless data experiences like Swiggy?

Let’s transform your database performance and reliability — so your customers never have to wait.

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